The design and manufacture of new products has become an increasingly complex activity due to the high performance required by the users of such products. Therefore, many products are designed to incorporate high performance signal processing and/or control schemes. The methods used in designing high performance signal processing and/or control schemes require mathematical models of the systems under consideration. Control and signal processing engineers construct these mathematical models using established modeling methods.
One family of methods for constructing models is known as system identification. In order to use system identification methods, system designers must rely on data gathered from experiments conducted on the system under consideration, as well as on prior knowledge of the behavior of the system. Most system identification methods are iterative and seek to improve model accuracy through repeated experiments and numerical computations. The resulting accurate models can be used to design high performance signal processing and/or control schemes for the systems under consideration.
An example where system identification is used is in communication systems. A key component of any analog or digital communication system is a communications channel. The communications channel is the medium through which a signal is transmitted and received. For a Digital Subscriber Line (DSL), the channel may include the analog transmitter electronics, the copper wiring that connects the central office and the customer modem, and the analog receiver electronics. For a wireless communications system, the channel may include the analog transmitter electronics, transmitting antenna, electromagnetic propagation to the receiving antenna, the receiving antenna itself, and analog receiver electronics. Accurate channel models play a critical role in analyzing and designing communications systems.
Several system identification methods are available to system designers. Many of these methods are encoded in existing system identification software tools. A typical example of a system identification tool is MATLAB® System Identification Toolbox, available from Mathworks, Inc., Natick, Mass., which is a state-of-the-art system identification package having a graphical user interface (GUI). To successfully use the MATLAB® System Identification Toolbox, the control system designer must interpret results and make numerous complex decisions in the areas of identification experiment design and refinement, experimental data quality analysis, and model quality analysis. These system identification tools thus require experienced users with specific expertise in system identification theory. In addition, these methods leave the construction and refinement of system identification experiments up to the user.
Therefore, what is needed is a tool for the identification of dynamic systems that automates the entire identification process. Further, the tool should require little or no specific knowledge of system identification theory from the control system designer who uses it.